Karl Thomas Hjelmervik

Papers from this author

Deep Learning on Active Sonar Data Using Bayesian Optimization for Hyperparameter Tuning

Henrik Berg, Karl Thomas Hjelmervik
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 14:00 in session PS T1.11

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Auto-TLDR; Bayesian Optimization for Sonar Operations in Littoral Environments

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Sonar operations in littoral environments may be challenging due to an increased probability of false alarms. Machine learning can be used to train classifiers that are able to filter out most of the false alarms automatically, however, this is a time consuming process, with many hyperparameters that need to be tuned in order to yield useful results. In this paper, Bayesian optimization is used to search for good values for some of the hyperparameters, like topology and training parameters, resulting in performance superior to earlier trial-and-error based training. Additionally, we analyze some of the parameters involved in the Bayesian optimization, as well as the resulting hyperparameter values.